Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis
Paul Bäcker,
Georg Maier,
Robin Gruna,
Thomas Längle,
Jürgen Beyerer
Kapitel/Beitrag aus dem Buch: Beyerer, J et al. 2023. OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings.
Polycyclic aromatic hydrocarbons (PAH) containing
tar-mixtures pose a challenge for recycling road rubble, as the
tar containing elements have to be extracted and decontaminated
for recycling. In this preliminary study, tar, bitumen and
minerals are discriminated using a combination of color (RGB)
and Hyperspectral Short Wave Infrared (SWIR) cameras. Further,
the use of an autoencoder for detecting minerals embedded
inside tar- and bitumen mixtures is proposed. Features are extracted
from the spectra of the SWIR camera and the texture of
the RGB images. For classification, linear discriminant analysis
combined with a k-nearest neighbor classification is used. First
results show a reliable detection of minerals and positive signs
for separability of tar and bitumen. This work is a foundation for
developing a sensor-based sorting system for physical separation
of tar contaminated samples in road rubble.